The present invention relates to sample analysis systems and, more particularly, to a mass sensor system for analyzing an unknown sample according to one or more temporal profiles of mass abundance derived from mass spectra.
Data representative of an unknown sample is generated by modern instruments for use in a wide variety of quantitative and qualitative data analyses.
Instrumentation for carrying out mass spectral analyses are known in the art for identifying one or more specific chemical components of a sample mixture according to a mass spectrum derived from the detection of a mass fragment pattern. In a typical procedure for mass spectral analysis, the sample is vaporized into a headspace volume, the concentration of vaporized components are deliberately allowed to equilibrate and stabilize, a portion of the vaporized components are withdrawn from the headspace at a predetermined time, and the vaporized portion is provided to the detection chamber in the mass spectrometer. The mass spectral analysis is then performed to generate a mass spectrum.
Often, at least one of two goals can be identified for such mass spectral data analysis: (1) comparing one or more samples to a standard having a known, or approved, composition so as to classify, if not identify, each sample; and (2) with regard to a sample that has been classified, providing an accurate identification of the component(s) in a sample that caused such sample to be classified as a differentiated, or anomalous complex sample.
To accomplish these goals, modern pattern recognition techniques have been used to interpret the data. For example, one object is to discern a pattern from the relative intensities of the sequence of peaks in the mass spectrum in a fashion sufficient to identify a xe2x80x9cchemical fingerprintxe2x80x9d. Chemical fingerprinting, whether interpreted by human intervention or automated pattern recognition in software, has been applied to identify a sample, to infer a property of interest (typically, adherence to a performance standard), or to classify the sample into one of several categories (good versus bad, Type A versus Type B, etc.). One field of study which encompasses this type of pattern recognition technology is called chemometrics.
However, the foregoing approach is typically performed using a sample portion withdrawn at one predetermined moment after equilibration of the volatile components in the headspace. This approach presumes that a mass spectrum of a sample taken at a predetermined time after equilibration affords sufficient mass spectral information to successfully identify, classify, or otherwise analyze the unknown sample.
We have determined that a mass spectrum derived at a predetermined time after equilibration provides less than sufficient mass spectral information to successfully identify, classify, or otherwise analyze the unknown sample.
According to the present invention, a method may be carried out for performing chemometric analysis of a sample, wherein the method includes the steps of: performing a plurality (k) of ion scan sequences over a predetermined time period and generating, in response, a respective plurality of (k) mass spectra, wherein each mass spectrum is representative of the mass sensor response to the vapor phase molecules during the time period, selecting a plurality of (n) active masses from the plurality of (k) mass spectra; compiling a distinctive, kxc3x97n vector matrix that is representative of the mass sensor response to the sample; and performing a chemometric data analysis of the kxc3x97n vector matrix using one or more selectable chemometric data analysis techniques, and, in response to the chemometric data analysis, reporting the results of the chemometric data analysis.